Joint Energy Management and Energy Trading in Residential Microgrid System

Abstract

The sustainability of the power systems assures consumers to have efficient and cost-effective energy consumption. Consumers' energy management is one of the solutions that in fact boosts the power system stability via efficiently scheduling the appliances. In addition to energy management, consumers fulfill their low-cost energy consumption using decentralized energy generation (such as solar, wind, plugin hybrid electric vehicles, and small diesel generator). This decentralized energy generation and its trading among the prosumers and consumers help in the distribution grid stability and continuous supply. In this paper, the joint energy management and energy trading model is presented, which provides low-cost electricity consumption to the distribution system. The proposed framework is a twofold system. In the first fold, the distribution system is divided into a number of microgrids, where each microgrid electricity demand is managed using a unified energy management approach. While the local energy produced is traded among the microgrids in the second fold, through energy trading concepts that fulfill the consumers' demand without stressing the utility company. The results indicate that the proposed model reduced the electricity cost of the microgrids with maximum share of self-generation. Moreover, the results also indicate that each microgrid either fulfills its electricity demand from self-generation or purchases it from the nearby microgrid. INDEX TERMS Smartgrid, Unified demand side management, Peak to average power ratio, Consumer comfort level.. Nomenclature β 1 , β 2 Set of appliances having various priorities, e.g., β 1 ∈ {Washing machine, dish washer} and β 2 ∈{Dryer, sterilizer}, etc. γ t Peak clipping maximum limit. λ t,n v,a Consumer preference factor A n Set of appliances of consumer's n.

A Comprehensive Control Strategy for Multibus Nanogrids With Power Exchange Between Prosumers

“…According to it, voltage measurements at PCC of prosumer 1 uPCC1 (the closest to the switch that connects the nanogrid to the distribution grid) and at the grid uG are expressed in αβ frame by means of a SOGI-PLL technique. Starting from the difference observed in voltage magnitude and in angular frequency, a correction of each parameter is obtained by PI controllers (10). These corrections are added to the reference voltage signal at the filter capacitor of each prosumer (u * C,PRIMi) that inputs the voltage controller (see Fig.…”

“…Moreover, the context for these studies is gridconnected (GC) microgrids or, at least, connected to other microgrids or resources as an energy community. As an example, authors in [10] consider jointly short-term energy management and energy trading in a GC microgrid. Several recent papers address energy trading between stakeholders in a microgrid operating in islanded mode (IS) [11], [12].…”

Microgrid Energy Management With Asynchronous Decentralized Particle Swarm Optimization

“…A second class of algorithms comprises decentralization of the optimization problem in autonomous problems within zones or regions with smaller scope, this is typical in the multi-microgrid scenario. A twofold system whit autonomous microgrids performing demand-side management by linear programming and a trading center calculating time of use tariffs is used in [6] to solve the joint energy management and energy trading model among microgrids to maximize self-generation. In [7], the global operation cost between several microgrids trading energy with each other is minimized by proposing an iterative and scalable distributed algorithm that first addresses the problem decentralization and then solves the sub-microgrid problem as a second step.…”

Intelligent Electric Power Management System for Economic Maximization in a Residential Prosumer Unit

“…Assuming a generalised approach focusing on developing a joint energy management and energy trading framework, in [20], the minimisation of the power system's electricity cost is proposed considering various factors that influence the power system stability and sustainability. In this case, consumer load profile, load shedding, heterogenous load, peak clipping, valley filling, human interaction, appliances priority, and consumer preferences to maximise the use of distributed energy resources.…”

“…Finally, Section VI contains the conclusions derived from this research. [12] x x [13] x x [14] x x [15] x [16] x [17] x x [18] x [19] x [20] [21]…”